Text Preprocessing In Python Code

R usually has the same but less. This tutorial is an introduction to optical character recognition (OCR) with Python and Tesseract 4. This post is an early draft of expanded work that will eventually appear on the District Data Labs Blog. ) Finding Libraries. Pre-Processing Layer So, lets first create layer that will utilize Embedding and Positional Encoding, we implemented in the previous article. I've done my best to make the article easy and simple as possible. If you find yourself running up against these issues frequently, you may have to resort to some pre-processing of the audio. We will be converting the text into numbers where each word will be represented by an array of numbers which can of different length depending upon the glove embedding you. sklearn’s classification report or any other way of model evaluation). Most of the KEYWORDS shown above are standard, and those that are not can be easily added to specialized Python code. The passed text will be encoded as UTF-8 by pybind11 before passed to the fastText C++ library. There are two main types of. Step 2: Data preprocessing ¶. The text preprocessing step involves tasks such as tokenization, stemming, and stop word removal. This new filename is passed up the chain to the top-level Python pipeline. The text is released under the CC-BY-NC-ND license, and code is released under the MIT license. from tensorflow. > Leverage Natural Language Processing (NLP) in Python and learn how to set up your own robust environment for performing text analytics. Flexible Data Ingestion. Most of the text book covers this topic in general, however in this Linear Discriminant Analysis – from Theory to Code tutorial we will understand both the mathematical derivations, as well how to implement as simple LDA using Python code. Table of Contents Random Forest Regression Using Python Sklearn From Scratch Recognise text and digit from the image with Python, OpenCV and Tesseract OCR Real-Time Object Detection Using YOLO Model Deep Learning Object Detection Model Using TensorFlow on Mac OS Sierra Anaconda Spyder Installation on Mac & Windows Install XGBoost on Mac OS. Text data preprocessing. The very act of pre-processing can even include the use of unsupervised machine learning to reduce the amount of features in total to increase speed and efficiency. Utilities for text input preprocessing. We walk step-by-step through an introduction to machine learning using Python and scikit-learn, explaining each concept and line of code along the way. Salah satu implementasi dari text mining adalah tahap Text Preprocessing. This node supports Python 2 and 3. This time, we use a Kaggle’s competition topic, Toxicity Classification , as our NLP example. Natural Language Tool Kit (NLTK) ¶ The most used library in social science is probably the “Natural Language Tool Kit”, normally referred to as “NLTK”. The NLTK Lemmatization method is based on WorldNet's built-in morph function. 1 (331 ratings) Course Ratings are calculated from individual students' ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately. The passed text will be encoded as UTF-8 by pybind11 before passed to the fastText C++ library. Here we used. In general, learning algorithms benefit from standardization of the data set. How can Python be used to handle ETL tasks for SQL Server with non-standard text files?. I would recommend practising these methods by applying them in machine learning/deep learning competitions. What are the types of automatic text summarization? The primary distinction of text summarization methods is whether they use the parts text itself, or can they generate new words and sentences. IPython notebook and NumPy can be used as a scratchpad for lighter work, while Python is a powerful tool for medium-scale data processing. This means it is important to use UTF-8 encoded text when building a model. The final step before using pytesseract for OCR is to write the pre-processed image, gray , to disk saving it with the filename from above ( Line 34 ). What is Text Classification? Document or text classification is used to classify information, that is, assign a category to a text; it can be a document, a tweet, a simple message, an email, and so on. In the first section, we'll discuss the OCR-A font, a font created specifically to aid Optical Character Recognition algorithms. Import libraries and modules. This involves more than just scaling and normalizing your data. GEO214 /409 /402 Python Data Processing Code Examples – Section I. Pillow is a powerful library, especially when used with Python. Here we used. The following are code examples for showing how to use keras. NLTK is a leading platform for building Python programs to work with human language data. Create a GUI to import a CSV file into Python. py (or, if you're in iPython, run makeModel. /:;<=>[email protected][\\]^_`{|}~\t\n', lower=True, split=' ') One-hot encodes a text into a list of word. It's taking approx. This repo contains a PyTorch implementation of a pretrained BERT model for multi-label text classification. In this simple tutorial we will learn to implement Data preprocessing in python. Computer Vision SDK Python Docs. These steps can be used for any text classification task. Tesseract is an excellent package that has been in development for decades, dating back to efforts in the 1970s by IBM, and most recently, by Google. Text Analytics with Python A Practitioner’s Guide to Natural Language Processing Any source code or other supplementary material referenced by the author in. Many thanks to ThinkNook for putting such a great resource out there. org) as assets into App Inventor. Natural Language Tool Kit (NLTK) ¶ The most used library in social science is probably the “Natural Language Tool Kit”, normally referred to as “NLTK”. The only downside might be that this Python implementation is not tuned for efficiency. Linear Discriminant Analysis (LDA) is an important tool in both Classification and Dimensionality Reduction technique. A brief aside: I include a lot of markdown documentation in my notebooks. This is the process of splitting a text into individual words or sequences of words (n-grams). LabelEncoder [source] ¶. Therefore code blocks are denoted by line indentation. 102: Pre-processing data: tokenization, stemming, and removal of stop words (compressed code) Michael Allen natural language processing December 15, 2018 2 Minutes In the previous code example ( here ) we went through each of the steps of cleaning text, showing what each step does. This article is an overview of some text summarization methods in Python. How to access table which is in web (using html) and how to get the data of the table using python 7 hours ago; How can I delete a file in Python IDLE? 3 days ago; How to write a program that counts number of characters, number of words, number of repeated words and number of repeated characters in a text file using opps concept in python 3. Matplotlib can be used in Python scripts, the Python and IPython shells, the Jupyter notebook, web application servers, and four graphical user interface toolkits. In this tutorial, I will walk through the steps to turn text data into a format that a machine can understand, share some of the most popular text analytics techniques, and showcase several natural language processing (NLP) libraries in Python including NLTK, TextBlob, spaCy and gensim. import string import numpy as np import nltk from nltk. I have a text file named 'data-science-wiki. February 16, 2011. In the first part, we will load our model and wri. The Python code examples are available as text files and also uploaded as assets. Dennyy Arthur Spirlingz Abstract Despite the popularity of unsupervised techniques for political science text-as-data. In this post you will discover how to prepare your data for machine learning in Python using scikit-learn. When building Machine Learning systems based on tweet data, a preprocessing is required. Another important preprocessing step is tokenization. We also saw how to go backward, from the one-hot encoded representation into the original text form. The following are code examples for showing how to use keras. If you’re comfortable with Python and its libraries, including pandas and scikit-learn, you’ll be able to ad. Conclusion In this tutorial, you learned some Natural Language Processing techniques to analyze text using the NLTK library in Python. Now we will see how we can implement this using sklearn in Python. TextRazor Python Reference. All you have to do is keep feeding it a thousand times more data. Preprocessing text data¶. Skulpt may be licensed under: The MIT license. Processing is a programming language, development environment, and online community. 2 minutes to process the tweets. I've done my best to make the article easy and simple as possible. What is Text Classification? Document or text classification is used to classify information, that is, assign a category to a text; it can be a document, a tweet, a simple message, an email, and so on. Dipanjan has also authored several books on R, Python, Machine Learning, and analytics, including Text Analytics with Python, Apress 2016. In particular, the submodule scipy. sub command and most of our text doesn't contain numbers. code, we have. As explained on wikipedia, tokenization is "the process of breaking a stream of text up into words, phrases, symbols, or other meaningful elements called tokens. preprocessing. Salah satu implementasi dari text mining adalah tahap Text Preprocessing. In this post I will try to give a very introductory view of some techniques that could be useful when you want to perform a basic analysis of opinions written in english. Many thanks to ThinkNook for putting such a great resource out there. My code block creates an empty Python list called ‘newList’ and then loops through each SUM([Profit]) supplied in another list to check if greater than 0. These steps can be used for any text classification task. He specializes in text analytics and search applications, and over the years, he has enjoyed working on a variety of information management and data science problems. Natural Language Processing with Java will explore how to automatically organize text using approaches such as full-text search, proper name recognition, clustering, tagging, information extraction, and summarization. I have written following python script to generate this dataset. The function of preprocessing is feature extraction and normalization, in general, it converts input data such as text for the machine learning algorithm. However, ignoring this, the package only has built-in dependencies from Python 3. There are two main types of. This guide will provide an example-filled introduction to data mining using Python, one of the most widely used data mining tools – from cleaning and data organization to applying machine learning algorithms. Try Visual Studio Code, our popular editor for building and debugging Python apps. · Implement exemplary applications related to Network Programming, Web Services and Databases in Python. In this example we take a look at bag of words, which contains words, and from the data, count the frequency of word occurs in the text. The "Operation-Location" field contains the URL that you must use for your Get Handwritten Text Operation Result operation. Paste it in the notebook in the variable “html_page”. Analyzing Unstructured Data with Text Mining There is a lot of unstructured data out there, such as news articles, customer feedbacks, Twitter tweets and so on, that contains information and needs to be analyzed. Preprocessing the data. In this blog post, we show how custom online prediction code helps maintain affinity between your preprocessing logic and your model, which is crucial to avoid training-serving skew. If you find this content useful, please consider supporting the work by buying the book!. R usually has the same but less. Now we will see how we can implement this using sklearn in Python. Machine Learning with Python Cookbook: Practical Solutions from Preprocessing to Deep Learning This practical guide provides nearly 200 self-contained recipes to help you solve machine learning challenges you may encounter in your daily work. Let’s consider the most noticeable: remove_stopwords() - remove all stopwords from string. It helps in returning the base or dictionary form of a word, which is known as the lemma. spaCy is the best way to prepare text for deep learning. There are other ways to implement one-hot encoding in python such as with Pandas data frames. In the previous episode, we have seen how to collect data from Twitter. Try my machine learning flashcards or Machine Learning with Python Cookbook. org) as assets into App Inventor. First, let's divide our dataset into features and label set. This class allows to vectorize a text corpus, by turning each text into either a sequence of integers (each integer being the index of a token in a dictionary) or into a vector where the coefficient for each token could be binary, based on word count, based on tf-idf num_words: the maximum number. Below are the code snippets and the descriptions of each block used to build the text classification model. For preprocessing, we are going to make a duplicate copy of our original dataframe. This means it is important to use UTF-8 encoded text when building a model. It was developed with a focus on enabling fast experimentation. You can vote up the examples you like or vote down the ones you don't like. Text summarization is a subdomain of Natural Language Processing (NLP) that deals with extracting summaries from huge chunks of texts. Different kind of data (images, text, sounds, videos, csv files, etc) have different methods for preprocessing, but there are some methods, which are common for almost any kind of data. I would recommend practising these methods by applying them in machine learning/deep learning competitions. Lets load in the Python dependencies: import pandas as pd import numpy as np from sklearn. by Kavita Ganesan How to extract keywords from text with TF-IDF and Python's Scikit-Learn Back in 2006, when I had to use TF-IDF for keyword extraction in Java, I ended up writing all of the code from scratch. Natural Language Tool Kit (NLTK) ¶ The most used library in social science is probably the “Natural Language Tool Kit”, normally referred to as “NLTK”. com, the author also referenced a github repository to where he obtained some Python code on preprocessing the fer2013 dataset. Deep Learning is everywhere. stem(w)) Now our result is: It is import to by veri pythonli while you are python with python. For the sake of clarity, suppose our input code is shown in the figure 3 below. Image processing in Python. text import Tokenizer. An exploratory analysis of prepared text data can be performed to understand its main characteristics, such as the main topic of the text and word frequency distributions. This will continue on that, if you haven't read it, read it here in order to have a proper grasp of the topics and concepts I am going to talk about in the article. Part II - Text data preprocessing. It was developed with a focus on enabling fast experimentation. Afterwards the documents are transformed into a bag of words, on which than various preprocessing nodes are applied, such as stop word filtering or stemming. , text file only has comments), below code will return a list of words. And here is the result of both of preprocessing tasks. ) by extracting text and barcode information. When you use the Recognize Text interface, the response contains a field called "Operation-Location". Lets load in the Python dependencies: import pandas as pd import numpy as np from sklearn. 7 specifically, it is possible to write Python code which is compatible with version 2. Small Python Trick: We use an if statement in the code below to check beforehand if a number exists in a text. Next, in Python Deep Learning Environment Setup Tutorial, we also discuss the Python Text Editor and Python Pip. Text classification is a very common use of neural networks and in the tutorial we will use classify movie reviews as positive or negative. NLTK is a leading platform for building Python programs to work with human language data. So to process on each sentence of the text what function and tool I should use. Machine Learning and the Titanic II Code October 15, 2019. toarray () method mean in python [on hold] What does the. These words are called **stopwords**, and they are most common function words such as 'is', 'the', 'that', or punctuation. preprocessing. The toolset offers many preprocessing algorithms and features that can be combined in many ways by YAML configuration files. Text Preprocessing For Unsupervised Learning: Why It Matters, When It Misleads, And What To Do About It Matthew J. Tesseract is an excellent package that has been in development for decades, dating back to efforts in the 1970s by IBM, and most recently, by Google. That’s the case with this file. Natural Language Processing (NLP) using Python is a certified course on text mining and Natural Language Processing with multiple industry projects, real datasets and mentor support. Python for Data Science For Dummies. There are two main types of. Get this from a library! Machine learning with Python cookbook : practical solutions from preprocessing to deep learning. You will need these to use the net. · Implement exemplary applications related to Network Programming, Web Services and Databases in Python. Text classifiers work by leveraging signals in the text to “guess” the most appropriate classification. In the previous post, we went through the concepts of different text pre-processing steps. preprocessing. Tokenizing raw text data is an important pre-processing step for many NLP methods. py to your. In addition, it also have the capability to act as a web crawler and able to retrieve information from twitter, facebook etc. This means it is important to use UTF-8 encoded text when building a model. How can I apply SMOTE to text classification using Python? In Preprocessing of Methods, there is a sentence "The informal words may be in the form of slang words or abbreviations that are. This article summarizes Python's text processing facilities for the programmer new to Python. In this tutorial, you will. Preprocessing the movie review text The movie review text needs to be preprocessed and converted to numerical tokens, corresponding to different words in the corpus. The text preprocessing step involves tasks such as tokenization, stemming, and stop word removal. 6) ☑ Support for Conda ☑ Install R and Python libraries directly from Dataiku’s interface ☑ Open environment to install any R or Python libraries ☑ Manage packages dependencies and create reproducible environments Scale code. It also allows to import Jupyter notebooks as Python modules via the knime_jupyter module that is part of the Python workspace. getpid to derive a temporary image filename based on the process ID of our Python script (Line 33). It is available free of charge and free of restriction. I hope that now you have a basic understanding of how to deal with text data in predictive modeling. My point is that we can use code (Python/Numpy etc. Ask Question Asked 23 days ago. Part II - Text data preprocessing. Python is a phenomenally good tool for text analysis, and there are a few good tools out there you can use. The dataset is a subset of data derived from the 2016 How ISIS Uses Twitter dataset, and the example demonstrates the importance of pre-processing in counting word frequencies from the tweets. LabelEncoder¶ class sklearn. Gensim runs on Linux, Windows and Mac OS X, and should run on any other platform that supports Python 2. This preprocessor takes the description of the semantics of a language (i. You have to provide your complete file path in open() command of Python if it stored in any other directory. Text data preprocessing in python. Or a billion. hwrt is short for ‘handwriting recognition toolkit’. ) to better understand abstract mathematical notions! Thinking by coding! 💥 We will start with basic but very useful concepts in data science and machine learning/deep learning like variance and covariance matrix and we will go further to some preprocessing techniques used to feed images. These steps can be used for any text classification task. This tutorial is an introduction to optical character recognition (OCR) with Python and Tesseract 4. Python is a great language for text processing. In this video, we are going to preprocess some data to make it suitable to train a model on. TransactionEncoder. Our main analysis endpoint offers a simple combined call that allows you to perform several different analyses on the same document, for example extracting b. Sass is completely compatible with all versions of CSS. Tesseract is an excellent package that has been in development for decades, dating back to efforts in the 1970s by IBM, and most recently, by Google. You can refer to this link for the complete implementation. NumPy for number crunching. Data Preprocessing is a HUGE topic, because the preprocessing techniques vary from data to data. - Natural Language Processing (Part 2): Data Cleaning & Text Pre-Processing in Python Text Generation with Markov Chains in Python All of the supporting Python code can be found here:. Also, I am using Anaconda and Spyder, but you can use any IDE that you prefer. Text summarization is a subdomain of Natural Language Processing (NLP) that deals with extracting summaries from huge chunks of texts. I want to process each sentence separately, means take a random text and then work on each sentence of that text to identify that which class is associated to each sentence of that text. The preprocessing package is dependent on NLTK for tokenizers and stopwords. We will also discuss text preprocessing tools. With that in mind, I thought of writing an article about different text preprocessing techniques. The "Operation-Location" field contains the URL that you must use for your Get Handwritten Text Operation Result operation. Utilities for text input preprocessing. ) Basically, Python can be seen as a dialect of Lisp with "traditional" syntax (what Lisp people call "infix" or "m-lisp" syntax). This code uses the tkinter module. Question 8: Read and run the Keras code for image preprocessing. Sass is completely compatible with all versions of CSS. You will need these to use the net. 4 The result of the 1st and 2nd preprocessing task – Intermediate representation –. Data Pre-processing is the first step in any machine learning model. Matplotlib is a Python 2D plotting library which produces publication quality figures in a variety of hard copy formats and interactive environments across platforms. Top 10 Python IDEs By Animikh Aich What is an IDE?IDE stands for Integrated Development Environment. I have thus decided to perform a step by step preprocessing of some textual data derived from Apple Appstore descriptions and a K-Means cluster of the resulting text. How to remove stop words from unstructured text data for machine learning in Python. I took all the 50k images in the CIFAR-10 dataset on Kaggle. I have covered text data preprocessing which was regarding Natural Language Processing. You can use the following code for normalization − data_normalized = preprocessing. words('english') cache_en_tweet_stopwords=stopwords. For each tag type (tagger) there is a corresponding filter node that filters terms with certain tag values assigned. ) to better understand abstract mathematical notions! Thinking by coding! 💥 We will start with basic but very useful concepts in data science and machine learning/deep learning like variance and covariance matrix and we will go further to some preprocessing techniques used to feed images. The corresponding label will be the sentiment of the tweet. Even code sometimes if you try to analyze Github comments for example; The diversity makes the whole thing tricky. sum() drop non required columns (pd. Pure Python PDF to text converter (Python This example shows how to extract text informations from a PDF file without the need of system dependent tools or code. Normalization by example Code September 30, 2019. In this course you will build MULTIPLE practical systems using natural language processing, or NLP - the branch of machine learning and data science that deals with text and speech. LabelEncoder¶ class sklearn. Depending on the OS, this system package (to be installed by the system administrator) is called “libpython-dev” or “python-devel”. If you are about to ask a "how do I do this in python" question, please try r/learnpython, the Python discord, or the #python IRC channel on FreeNode. Rescaling Data¶ One issue with classification algorithms is that some of them are biased depending on how close data points are in their parameter space. preprocessing. In the previous episode, we have seen how to collect data from Twitter. Related courses. Installation. Pure Python PDF to text converter (Python This example shows how to extract text informations from a PDF file without the need of system dependent tools or code. It is available free of charge and free of restriction. A very simple way to do this would be to split the document by white space, including ” “, new lines, tabs and more. What are the types of automatic text summarization? The primary distinction of text summarization methods is whether they use the parts text itself, or can they generate new words and sentences. > Leverage Natural Language Processing (NLP) in Python and learn how to set up your own robust environment for performing text analytics. This computational experimentation has enabled the design and development of a. Data preprocessing. It can do the following preprocessing: lowercase all words: tolower=T; discard all words with a count lower than, say, 10: lower = 10; The result is a vector with names on the. Time is precious. Data preprocessing is nothing but the readying of data for experimentation- transforming raw data for further processing. 2 minutes to process the tweets. NLTK is a huge package with many natural language modules. The hash function employed is the signed 32-bit version of Murmurhash3. Ask Question Asked 23 days ago. In this simple tutorial we will learn to implement Data preprocessing in python. words('english_tweet') # For clarity, df is a pandas dataframe with a column['text'] together with other headers. This code uses the tkinter module. Our Python program will translate an arbitrary text into a musical score. Skulpt may be licensed under: The MIT license. The dataset is a subset of data derived from the 2016 How ISIS Uses Twitter dataset, and the example demonstrates the importance of pre-processing in counting word frequencies from the tweets. Python tool works like any other tool in Alteryx and if you are re-opening the workflow then you need to execute the workflow to get the valid output from it. Tokenizing Raw Text in Python. It will save augmented images in a folder called “preview” on the. We’ll start by creating a simple dataset that we can use to evaluate our text skew corrector. Natural Language Processing (NLP) Using Python Natural Language Processing (NLP) is the art of extracting information from unstructured text. sub command and most of our text doesn't contain numbers. Structure of the code. The PDF version can be downloaded from HERE. Please note that this dual license only applies to the part of Skulpt that is included in the runtime, and not necessarily to surrounding code for build processing or testing. There are many more options for pre-processing which we'll explore. drop()) Features Engineering. In natural language processing, useless words (data), are referred to as stop words. You have to provide your complete file path in open() command of Python if it stored in any other directory. NLP and Text mining with python(for absolute beginners only) 4. So to process on each sentence of the text what function and tool I should use. The following are code examples for showing how to use keras. Proses preprocessing ini meliputi (1) case folding, (2) tokenizing, (3) filtering, dan (4) stemming. text_to_word_sequence(). This guide will provide an example-filled introduction to data mining using Python, one of the most widely used data mining tools – from cleaning and data organization to applying machine learning algorithms. They are extracted from open source Python projects. You might want to call it something like databaseconfig. Hi all, I'm pretty new to CV, I'm doing some experiment that requires OCR, I'm ussing tesseract as the OCR Engine. Besides the regular preprocessing nodes, such as stemming, stop word filtering, etc. Data preprocessing. Text cleaning could be a tedious process, and knowing the correct approach is the key to get the designed outcome. Text data preprocessing in python. How can I apply SMOTE to text classification using Python? In Preprocessing of Methods, there is a sentence “The informal words may be in the form of slang words or abbreviations that are. Import libraries and modules. text_to_word_sequence(): Converts a text to a sequence of words (or tokens. Before you can read, append or write to a file, you will first have to it using Python's built-in open() function. They are extracted from open source Python projects. note: for the new pytorch-pretrained-bert package. Credit Card OCR with OpenCV and Python. text_to_word_sequence(). From this pipeline we can extract any component, but here we’re going to access sentence tokens using the sentencizer component. This Doc object uses our preprocessing pipeline’s components tagger,parser and entity recognizer to break the text down into components. preprocessing. Easy Natural Language Processing (NLP) in Python. measure import compare_ssim as ssim from Stack Exchange Network Stack Exchange network consists of 175 Q&A communities including Stack Overflow , the largest, most trusted online community for developers to learn, share their knowledge, and build their careers. Text classification is a very common use of neural networks and in the tutorial we will use classify movie reviews as positive or negative. Ask Question Asked 23 days ago. preprocessing import LabelEncoder, StandardScaler from sklearn. Utilities for text input preprocessing. You will also receive a free Computer Vision Resource guide. Sentiment analysis is a method of analyzing a piece of text and deciding whether the writing is positive, negative or neutral. Therefore, if you are just stepping into this field. build_preprocessor (self) [source] ¶ Return a function to preprocess the text before tokenization. Clean text often means a list of words or tokens that we can work with in our machine learning models. Code Create and use custom code environments ☑ Support for multiple versions of Python (2. Most of the data obtained from the websites and other sources are text data and thus, it is required to process them in a different manner. py extension, for example prefoo. This is the process of splitting a text into individual words or sequences of words (n-grams). Tutorial: Simple Text Classification with Python and TextBlob Aug 26, 2013 Yesterday, TextBlob 0. In this article, we will see how we can use automatic text summarization techniques to summarize text data. Part II - Text data preprocessing. 2 minutes to process the tweets. If you find this content useful, please consider supporting the work by buying the book!. That’s the case with this file. The Python Discord. preprocessing. Most of the text book covers this topic in general, however in this Linear Discriminant Analysis – from Theory to Code tutorial we will understand both the mathematical derivations, as well how to implement as simple LDA using Python code. > Leverage Natural Language Processing (NLP) in Python and learn how to set up your own robust environment for performing text analytics. This guide will be split into two parts. GEO214 /409 /402 Python Data Processing Code Examples – Section I. These steps can be used for any text classification task. Create a Python script that analyzes poll data from multiple counties and prints the results to the terminal as well as exports a text file with the results. This project consists of a C preprocessor which allows to use a python inspired syntax to write C code, as well as much more powerful preprocessing using python instead of the very limited standard C preprocessor. They are extracted from open source Python projects. The fact-checkers, whose work is more and more important for those who prefer facts over lies, police the line between fact and falsehood on a day-to-day basis, and do a great job. Today, my small contribution is to pass along a very good overview that reflects on one of Trump’s favorite overarching falsehoods. Namely: Trump describes an America in which everything was going down the tubes under  Obama, which is why we needed Trump to make America great again. And he claims that this project has come to fruition, with America setting records for prosperity under his leadership and guidance. “Obama bad; Trump good” is pretty much his analysis in all areas and measurement of U.S. activity, especially economically. Even if this were true, it would reflect poorly on Trump’s character, but it has the added problem of being false, a big lie made up of many small ones. Personally, I don’t assume that all economic measurements directly reflect the leadership of whoever occupies the Oval Office, nor am I smart enough to figure out what causes what in the economy. But the idea that presidents get the credit or the blame for the economy during their tenure is a political fact of life. Trump, in his adorable, immodest mendacity, not only claims credit for everything good that happens in the economy, but tells people, literally and specifically, that they have to vote for him even if they hate him, because without his guidance, their 401(k) accounts “will go down the tubes.” That would be offensive even if it were true, but it is utterly false. The stock market has been on a 10-year run of steady gains that began in 2009, the year Barack Obama was inaugurated. But why would anyone care about that? It’s only an unarguable, stubborn fact. Still, speaking of facts, there are so many measurements and indicators of how the economy is doing, that those not committed to an honest investigation can find evidence for whatever they want to believe. Trump and his most committed followers want to believe that everything was terrible under Barack Obama and great under Trump. That’s baloney. Anyone who believes that believes something false. And a series of charts and graphs published Monday in the Washington Post and explained by Economics Correspondent Heather Long provides the data that tells the tale. The details are complicated. Click through to the link above and you’ll learn much. But the overview is pretty simply this: The U.S. economy had a major meltdown in the last year of the George W. Bush presidency. Again, I’m not smart enough to know how much of this was Bush’s “fault.” But he had been in office for six years when the trouble started. So, if it’s ever reasonable to hold a president accountable for the performance of the economy, the timeline is bad for Bush. GDP growth went negative. Job growth fell sharply and then went negative. Median household income shrank. The Dow Jones Industrial Average dropped by more than 5,000 points! U.S. manufacturing output plunged, as did average home values, as did average hourly wages, as did measures of consumer confidence and most other indicators of economic health. (Backup for that is contained in the Post piece I linked to above.) Barack Obama inherited that mess of falling numbers, which continued during his first year in office, 2009, as he put in place policies designed to turn it around. By 2010, Obama’s second year, pretty much all of the negative numbers had turned positive. By the time Obama was up for reelection in 2012, all of them were headed in the right direction, which is certainly among the reasons voters gave him a second term by a solid (not landslide) margin. Basically, all of those good numbers continued throughout the second Obama term. The U.S. GDP, probably the single best measure of how the economy is doing, grew by 2.9 percent in 2015, which was Obama’s seventh year in office and was the best GDP growth number since before the crash of the late Bush years. GDP growth slowed to 1.6 percent in 2016, which may have been among the indicators that supported Trump’s campaign-year argument that everything was going to hell and only he could fix it. During the first year of Trump, GDP growth grew to 2.4 percent, which is decent but not great and anyway, a reasonable person would acknowledge that — to the degree that economic performance is to the credit or blame of the president — the performance in the first year of a new president is a mixture of the old and new policies. In Trump’s second year, 2018, the GDP grew 2.9 percent, equaling Obama’s best year, and so far in 2019, the growth rate has fallen to 2.1 percent, a mediocre number and a decline for which Trump presumably accepts no responsibility and blames either Nancy Pelosi, Ilhan Omar or, if he can swing it, Barack Obama. I suppose it’s natural for a president to want to take credit for everything good that happens on his (or someday her) watch, but not the blame for anything bad. Trump is more blatant about this than most. If we judge by his bad but remarkably steady approval ratings (today, according to the average maintained by 538.com, it’s 41.9 approval/ 53.7 disapproval) the pretty-good economy is not winning him new supporters, nor is his constant exaggeration of his accomplishments costing him many old ones). I already offered it above, but the full Washington Post workup of these numbers, and commentary/explanation by economics correspondent Heather Long, are here. On a related matter, if you care about what used to be called fiscal conservatism, which is the belief that federal debt and deficit matter, here’s a New York Times analysis, based on Congressional Budget Office data, suggesting that the annual budget deficit (that’s the amount the government borrows every year reflecting that amount by which federal spending exceeds revenues) which fell steadily during the Obama years, from a peak of $1.4 trillion at the beginning of the Obama administration, to $585 billion in 2016 (Obama’s last year in office), will be back up to $960 billion this fiscal year, and back over $1 trillion in 2020. (Here’s the New York Times piece detailing those numbers.) Trump is currently floating various tax cuts for the rich and the poor that will presumably worsen those projections, if passed. As the Times piece reported: